501
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Liu W, Kohn N, Fernández G. Intersubject similarity of personality is associated with intersubject similarity of brain connectivity patterns. Neuroimage 2018; 186:56-69. [PMID: 30389630 DOI: 10.1016/j.neuroimage.2018.10.062] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 10/16/2018] [Accepted: 10/24/2018] [Indexed: 11/25/2022] Open
Abstract
Personality is a central high-level psychological concept that defines individual human beings and has been associated with a variety of real-world outcomes (e.g., mental health and academic performance). Using 2 h, high resolution, functional magnetic resonance imaging (fMRI) resting state data of 984 (primary dataset N = 801, hold-out dataset N = 183) participants from the Human Connectome Project (HCP), we investigated the relationship between personality (five-factor model, FFM) and intrinsic whole-brain functional connectome. We found a pattern of functional brain connectivity ("global personality network") related to personality traits. Consistent with the heritability of personality traits, the connectivity strength of this global personality network is also heritable (more similar between monozygotic twin pairs compared to the dizygotic twin pairs). Validated by both the repeated family-based 10-fold cross-validation and hold-out dataset, our intersubject network similarity analysis allowed us to identify participants' pairs with similar personality profiles. Across all the identified pairs of participants, we found a positive correlation between the network similarity and personality similarity, supporting our "similar brain, similar personality" hypothesis. Furthermore, the global personality network can be used to predict the individual subject's responses in the personality questionnaire on an item level. In sum, based on individual brain connectivity pattern, we could predict different facets of personality, and this prediction is not based on localized regions, but rather relies on the individual connectivity pattern in large-scale brain networks.
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Affiliation(s)
- Wei Liu
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, the Netherlands.
| | - Nils Kohn
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, the Netherlands
| | - Guillén Fernández
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, the Netherlands
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502
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Fountain-Zaragoza S, Samimy S, Rosenberg MD, Prakash RS. Connectome-based models predict attentional control in aging adults. Neuroimage 2018; 186:1-13. [PMID: 30394324 DOI: 10.1016/j.neuroimage.2018.10.074] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 10/24/2018] [Accepted: 10/26/2018] [Indexed: 12/23/2022] Open
Abstract
There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18-30) and older adults (n = 21, ages 60-80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.
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Affiliation(s)
| | - Shaadee Samimy
- Department of Psychology, The Ohio State University, USA
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503
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Functional connectivity of specific resting-state networks predicts trust and reciprocity in the trust game. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2018; 19:165-176. [DOI: 10.3758/s13415-018-00654-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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504
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Xie H, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Calhoun VD, Chen G, Damaraju E, Liu X, Mitra S. Time-varying whole-brain functional network connectivity coupled to task engagement. Netw Neurosci 2018; 3:49-66. [PMID: 30793073 PMCID: PMC6326730 DOI: 10.1162/netn_a_00051] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 03/16/2018] [Indexed: 11/30/2022] Open
Abstract
Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A. Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Xiangyu Liu
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
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505
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Zang Z, Geiger LS, Braun U, Cao H, Zangl M, Schäfer A, Moessnang C, Ruf M, Reis J, Schweiger JI, Dixson L, Moscicki A, Schwarz E, Meyer-Lindenberg A, Tost H. Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism. Netw Neurosci 2018; 2:464-480. [PMID: 30320294 PMCID: PMC6175691 DOI: 10.1162/netn_a_00045] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 01/11/2018] [Indexed: 01/21/2023] Open
Abstract
Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks. Learning a new motor skill prompts immediate reconfigurations of distributed brain networks followed by adaptive changes in intrinsic brain circuits related to synaptic plasticity. Here, we identify global brain network properties and a cerebellar-cortical functional subnetwork that are both significantly associated with motor learning ability in a previously trained visuomotor task in humans. We further show that the associated functional subnetwork connectivity but not the global brain network properties are susceptible to ketamine. Our findings suggest a distinct functional role for learning-related global versus local network metrics and support the idea of a preferential susceptibility of learning-associated subnetworks to N-methyl-d-aspartate antagonist and plasticity-related consolidation effects.
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Affiliation(s)
- Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Lena S Geiger
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Hengyi Cao
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Maria Zangl
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Axel Schäfer
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Matthias Ruf
- Department of Neuroimaging, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Janine Reis
- Department of Neurology and Neurophysiology, Albert-Ludwigs-University, Freiburg, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Luanna Dixson
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Alexander Moscicki
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
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506
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Cheng W, Rolls ET, Ruan H, Feng J. Functional Connectivities in the Brain That Mediate the Association Between Depressive Problems and Sleep Quality. JAMA Psychiatry 2018; 75:1052-1061. [PMID: 30046833 PMCID: PMC6233808 DOI: 10.1001/jamapsychiatry.2018.1941] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
IMPORTANCE Depression is associated with poor sleep quality. Understanding the neural connectivity that underlies both conditions and mediates the association between them is likely to lead to better-directed treatments for depression and associated sleep problems. OBJECTIVE To identify the brain areas that mediate the association of depressive symptoms with poor sleep quality and advance understanding of the differences in brain connectivity in depression. DESIGN, SETTING, AND PARTICIPANTS This study collected data from participants in the Human Connectome Project using the Adult Self-report of Depressive Problems portion of the Achenbach Adult Self-Report for Ages 18-59, a survey of self-reported sleep quality, and resting-state functional magnetic resonance imaging. Cross-validation of the sleep findings was conducted in 8718 participants from the UK Biobank. MAIN OUTCOMES AND MEASURES Correlations between functional connectivity, scores on the Adult Self-Report of Depressive Problems, and sleep quality. RESULTS A total of 1017 participants from the Human Connectome Project (of whom 546 [53.7%] were female; age range, 22 to 35 years) drawn from a general population in the United States were included. The Depressive Problems score was positively correlated with poor sleep quality (r = 0.371; P < .001). A total of 162 functional connectivity links involving areas associated with sleep, such as the precuneus, anterior cingulate cortex, and the lateral orbitofrontal cortex, were identified. Of these links, 39 were also associated with the Depressive Problems scores. The brain areas with increased functional connectivity associated with both sleep and Depressive Problems scores included the lateral orbitofrontal cortex, dorsolateral prefrontal cortex, anterior and posterior cingulate cortices, insula, parahippocampal gyrus, hippocampus, amygdala, temporal cortex, and precuneus. A mediation analysis showed that these functional connectivities underlie the association of the Depressive Problems score with poor sleep quality (β = 0.0139; P < .001). CONCLUSIONS AND RELEVANCE The implication of these findings is that the increased functional connectivity between these brain regions provides a neural basis for the association between depression and poor sleep quality. An important finding was that the Depressive Problems scores in this general population were correlated with functional connectivities between areas, including the lateral orbitofrontal cortex, cingulate cortex, precuneus, angular gyrus, and temporal cortex. The findings have implications for the treatment of depression and poor sleep quality.
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Affiliation(s)
- Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Edmund T. Rolls
- Department of Computer Science, University of Warwick, Coventry, United Kingdom,Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
| | - Hongtao Ruan
- School of Mathematical Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China,Department of Computer Science, University of Warwick, Coventry, United Kingdom,School of Mathematical Sciences, Fudan University, Shanghai, China,School of Life Science, Fudan University, Shanghai, China,The Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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507
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Miraglia F, Vecchio F, Rossini PM. Brain electroencephalographic segregation as a biomarker of learning. Neural Netw 2018; 106:168-174. [DOI: 10.1016/j.neunet.2018.07.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 07/05/2018] [Accepted: 07/09/2018] [Indexed: 01/11/2023]
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508
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The ebb and flow of attention: Between-subject variation in intrinsic connectivity and cognition associated with the dynamics of ongoing experience. Neuroimage 2018; 185:286-299. [PMID: 30266263 DOI: 10.1016/j.neuroimage.2018.09.069] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 08/14/2018] [Accepted: 09/24/2018] [Indexed: 01/30/2023] Open
Abstract
Cognition is dynamic, allowing us the flexibility to shift focus from different aspects of the environment, or between internally- and externally-oriented trains of thought. Although we understand how individuals switch attention across different tasks, the neurocognitive processes that underpin the dynamics of less constrained elements of cognition are less well understood. To explore this issue, we developed a paradigm in which participants intermittently responded to external events across two conditions that systematically vary in their need for updating working memory based on information in the external environment. This paradigm distinguishes the influences on cognition that emerge because of demands placed by the task (sustained) from changes that result from the time elapsed since the last task response (transient). We used experience sampling to identify dynamic changes in ongoing cognition in this paradigm, and related between subject variation in these measures to variations in the intrinsic organisation of large-scale brain networks. We found systems important for attention were involved in the regulation of off-task thought. Coupling between the ventral attention network and regions of primary motor cortex was stronger for individuals who were able to regulate off-task thought in line with the demands of the task. This pattern of coupling was linked to greater task-related thought when environmental demands were high and elevated off-task thought when demands were low. In contrast, the coupling of the dorsal attention network with a region of lateral visual cortex was stronger for individuals for whom off-task thoughts transiently increased with the time since responding to the external world . This pattern is consistent with a role for this system in the time-limited top-down biasing of visual processing to increase behavioural efficiency. Unlike the attention networks, coupling between regions of the default mode network and dorsal occipital cortex was weaker for individuals for whom the level of detail decreased with the passage of time when the external task did not require continuous monitoring of external information. These data provide novel evidence for how neural systems vary across subjects and may underpin individual variation in the dynamics of thought, linking attention systems to the maintenance of task-relevant information, and the default mode network to supporting experiences with vivid detail.
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509
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Kranz MB, Voss MW, Cooke GE, Banducci SE, Burzynska AZ, Kramer AF. The cortical structure of functional networks associated with age-related cognitive abilities in older adults. PLoS One 2018; 13:e0204280. [PMID: 30240409 PMCID: PMC6150534 DOI: 10.1371/journal.pone.0204280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Accepted: 09/04/2018] [Indexed: 01/15/2023] Open
Abstract
Age and cortical structure are both associated with cognition, but characterizing this relationship remains a challenge. A popular approach is to use functional network organization of the cortex as an organizing principle for post-hoc interpretations of structural results. In the current study, we introduce two complimentary approaches to structural analyses that are guided by a-priori functional network maps. Specifically, we systematically investigated the relationship of cortical structure (thickness and surface area) of distinct functional networks to two cognitive domains sensitive to age-related decline thought to rely on both common and distinct processes (executive function and episodic memory) in older adults. We quantified the cortical structure of individual functional network's predictive ability and spatial extent (i.e., number of significant regions) with cognition and its mediating role in the age-cognition relationship. We found that cortical thickness, rather than surface area, predicted cognition across the majority of functional networks. The default mode and somatomotor network emerged as particularly important as they appeared to be the only two networks to mediate the age-cognition relationship for both cognitive domains. In contrast, thickness of the salience network predicted executive function and mediated the age-cognition relationship for executive function. These relationships remained significant even after accounting for global cortical thickness. Quantifying the number of regions related to cognition and mediating the age-cognition relationship yielded similar patterns of results. This study provides a potential approach to organize and describe the apparent widespread regional cortical structural relationships with cognition and age in older adults.
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Affiliation(s)
- Michael B. Kranz
- Department of Psychology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
| | - Michelle W. Voss
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States of America
| | - Gillian E. Cooke
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
| | - Sarah E. Banducci
- Department of Psychology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
| | - Agnieszka Z. Burzynska
- Department of Human Development and Family Studies/ Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, United States of America
| | - Arthur F. Kramer
- Department of Psychology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
- Departments of Psychology and Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States of America
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510
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Sudre G, Mangalmurti A, Shaw P. Growing out of attention deficit hyperactivity disorder: Insights from the 'remitted' brain. Neurosci Biobehav Rev 2018; 94:198-209. [PMID: 30194962 DOI: 10.1016/j.neubiorev.2018.08.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 12/14/2022]
Abstract
We consider developmental and cognitive models to explain why some children 'grow out' of attention deficit hyperactivity disorder (ADHD) by adulthood. The first model views remission as a convergence towards more typical brain function and structure. In support, some studies find that adult remitters are indistinguishable from those who were never affected in the neural substrates of 'top-down' mechanisms of cognitive control, some 'bottom-up' processes of vigilance/response preparation, prefrontal cortical morphology and intrinsic functional connectivity. A second model postulates that remission is driven by the recruitment of new brain systems that compensate for ADHD symptoms. It draws support from demonstrations of atypical, but possibly beneficial, patterns of connectivity within the cognitive control network in adult remitters. The final model holds that some childhood ADHD anomalies show lifelong persistence, regardless of adult outcome, supported by shared reports of anomalies in remitters and persisters in posterior cerebral and striato-thalamic regions. The models are compatible: different processes driving remission might occur in different brain regions. These models provide a framework for future studies which might inform novel treatments to 'accelerate' remission.
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Affiliation(s)
- Gustavo Sudre
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and The National Institute of Mental Health, NIH, United States
| | - Aman Mangalmurti
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and The National Institute of Mental Health, NIH, United States
| | - Philip Shaw
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and The National Institute of Mental Health, NIH, United States.
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511
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Guimarães-da-Silva PO, Rovaris DL, Silva KL, Karam RG, Rohde LA, Grevet EH, Bau CHD. Exploring neuropsychological predictors of ADHD remission or persistence during adulthood. Cogn Neuropsychiatry 2018; 23:321-328. [PMID: 30092701 DOI: 10.1080/13546805.2018.1506324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Paula O Guimarães-da-Silva
- a Department of Psychiatry, Faculdade de Medicina , Universidade Federal do Rio Grande do Sul , Porto Alegre , Brazil.,b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil
| | - Diego L Rovaris
- a Department of Psychiatry, Faculdade de Medicina , Universidade Federal do Rio Grande do Sul , Porto Alegre , Brazil.,b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil
| | - Katiane L Silva
- b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil.,c CESUCA-Faculdade Inedi , Cachoeirinha , Brazil
| | - Rafael G Karam
- b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil
| | - Luis A Rohde
- a Department of Psychiatry, Faculdade de Medicina , Universidade Federal do Rio Grande do Sul , Porto Alegre , Brazil.,b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , Porto Alegre , Brazil
| | - Eugenio H Grevet
- a Department of Psychiatry, Faculdade de Medicina , Universidade Federal do Rio Grande do Sul , Porto Alegre , Brazil.,b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil
| | - Claiton H D Bau
- a Department of Psychiatry, Faculdade de Medicina , Universidade Federal do Rio Grande do Sul , Porto Alegre , Brazil.,b ADHD Outpatient Program - Adult Division , Hospital de Clínicas de Porto Alegre , Porto Alegre , Brazil.,e Department of Genetics, Instituto de Biociências, Universidade Federal do Rio Grande do Sul , Porto Alegre , Brazil
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512
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Yamamoto M, Kushima I, Suzuki R, Branko A, Kawano N, Inada T, Iidaka T, Ozaki N. Aberrant functional connectivity between the thalamus and visual cortex is related to attentional impairment in schizophrenia. Psychiatry Res Neuroimaging 2018; 278:35-41. [PMID: 29981940 DOI: 10.1016/j.pscychresns.2018.06.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/13/2018] [Accepted: 06/15/2018] [Indexed: 11/20/2022]
Abstract
Resting-state (rs) functional magnetic resonance imaging (fMRI) studies have revealed dysfunctional thalamocortical functional connectivity (FC) in schizophrenia. However, the relationship between thalamocortical FC and cognitive impairment has not been thoroughly investigated. We hypothesized that aberrant thalamocortical FC is related to attention deficits in schizophrenia. Thirty-eight patients with schizophrenia and 38 matched healthy controls underwent rs-fMRI and task fMRI while performing a Flanker task. We observed decreased left thalamic activation in patients with schizophrenia using task fMRI to determine the thalamic seed. A seed-based analysis using this seed was performed in the whole brain to assess differences in thalamocortical FC between the groups. Significantly worse performance was observed in the patient group. The rs-fMRI analysis revealed significantly increased FC between the left thalamus seed and the occipital cortices/postcentral gyri in patients when compared to controls. In the patient group, significant positive correlations were observed between the degree of FC from the left thalamus to the bilateral occipital gyri, which correspond to the visual cortex, and the Flanker effect. No significant correlation was detected in the control group. These results indicate that aberrant FC between the left thalamus and the visual cortex is related to attention deficits in schizophrenia.
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Affiliation(s)
- Maeri Yamamoto
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan; Institute for Advanced Research, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Ryohei Suzuki
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan
| | - Aleksic Branko
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan
| | - Naoko Kawano
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan; Institutes of Innovation for Future Society, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Toshiya Inada
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan
| | - Tetsuya Iidaka
- Department of Physical and Occupational Therapy, Nagoya University, Graduate School of Medicine, 1-1-20, Daiko-minami, Higashi, Nagoya, Aichi 461-8673, Japan.
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University, Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8560, Japan
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513
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Jiang R, Calhoun VD, Zuo N, Lin D, Li J, Fan L, Qi S, Sun H, Fu Z, Song M, Jiang T, Sui J. Connectome-based individualized prediction of temperament trait scores. Neuroimage 2018; 183:366-374. [PMID: 30125712 DOI: 10.1016/j.neuroimage.2018.08.038] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 08/13/2018] [Accepted: 08/16/2018] [Indexed: 12/16/2022] Open
Abstract
Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals' innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA; Dept. of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongdong Lin
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Hailun Sun
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zening Fu
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; University of Electronic Science and Technology of China, Chengdu, 610054, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
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514
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Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants. NEUROIMAGE-CLINICAL 2018; 20:407-414. [PMID: 30128279 PMCID: PMC6096053 DOI: 10.1016/j.nicl.2018.08.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 07/18/2018] [Accepted: 08/09/2018] [Indexed: 01/11/2023]
Abstract
Background Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n = 306) exploring the effect of antidepressant administration on emotional face processing. Methods We created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies. Results We found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50 to 87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another. Conclusions We found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development. The emotional faces task can evaluate the brain's fMRI response to antidepressants. We found a consistent signature for emotional valence across 11 such datasets. We found limited evidence for consistent antidepressant response. Case-controlled designs and more standardized protocols could increase this yield.
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515
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Sui J, Qi S, van Erp TGM, Bustillo J, Jiang R, Lin D, Turner JA, Damaraju E, Mayer AR, Cui Y, Fu Z, Du Y, Chen J, Potkin SG, Preda A, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen SC, O'Leary DS, McMahon A, Jiang T, Calhoun VD. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat Commun 2018; 9:3028. [PMID: 30072715 PMCID: PMC6072778 DOI: 10.1038/s41467-018-05432-w] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 07/04/2018] [Indexed: 01/06/2023] Open
Abstract
Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results highlight the salience network (gray matter, GM), corpus callosum (fractional anisotropy, FA), central executive and default-mode networks (fractional amplitude of low-frequency fluctuation, fALFF) as modality-specific biomarkers of generalized cognition. FALFF features are found to be more sensitive to cognitive domain differences, while the salience network in GM and corpus callosum in FA are highly consistent and predictive of multiple cognitive domains. These modality-specific brain regions define-in three separate cohorts-promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- The Mind Research Network, Albuquerque, NM, 87106, USA.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Dongdong Lin
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, 87106, USA
- Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, 30302, USA
| | | | - Andrew R Mayer
- The Mind Research Network, Albuquerque, NM, 87106, USA
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA
- San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA
- San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - James Voyvodic
- Department of Radiology, Brain Imaging and Analysis Center, Duke University, Durham, NC, 27710, USA
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Sarah C McEwen
- Department of Psychiatry, University of California, San Diego, CA, 92093, USA
| | - Daniel S O'Leary
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa, IA, 52242, USA
| | - Agnes McMahon
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, San Diego, CA, 90033, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, 87106, USA.
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA.
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA.
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516
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Cohen D, Tsuchiya N. The Effect of Common Signals on Power, Coherence and Granger Causality: Theoretical Review, Simulations, and Empirical Analysis of Fruit Fly LFPs Data. Front Syst Neurosci 2018; 12:30. [PMID: 30090060 PMCID: PMC6068358 DOI: 10.3389/fnsys.2018.00030] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 06/18/2018] [Indexed: 11/22/2022] Open
Abstract
When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. As a result, our understanding of how common signals affect neural connectivity estimation is incomplete. For example, little is known about recording preparations involving high spatial-resolution electrodes, used in linear array recordings. We address this gap through a combination of theoretical review, simulations, and empirical analysis of local field potentials recorded from the brains of fruit flies. We demonstrate how a framework that jointly analyzes power, coherence, and quantities based on Granger causality reveals the presence of common signals. We further show that subtracting spatially adjacent signals (bipolar derivations) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. We also show that Granger causality is adversely affected by common signals and that a quantity referred to as “instantaneous interaction” is increased in the presence of common signals. The theoretical review, simulation, and empirical analysis we present can readily be adapted by others to investigate the nature of the common signals in their data. Our contributions improve our understanding of how common signals affect power, coherence, and Granger causality and will help reduce the misinterpretation of functional and effective connectivity analysis.
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Affiliation(s)
- Dror Cohen
- School of Psychological Sciences, Monash University, Melbourne, VIC, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Melbourne, VIC, Australia
| | - Naotsugu Tsuchiya
- School of Psychological Sciences, Monash University, Melbourne, VIC, Australia.,Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Melbourne, VIC, Australia
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517
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Kragel PA, Koban L, Barrett LF, Wager TD. Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging. Neuron 2018; 99:257-273. [PMID: 30048614 PMCID: PMC6296466 DOI: 10.1016/j.neuron.2018.06.009] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 06/01/2018] [Accepted: 06/05/2018] [Indexed: 01/22/2023]
Abstract
Human neuroimaging research has transitioned from mapping local effects to developing predictive models of mental events that integrate information distributed across multiple brain systems. Here we review work demonstrating how multivariate predictive models have been utilized to provide quantitative, falsifiable predictions; establish mappings between brain and mind with larger effects than traditional approaches; and help explain how the brain represents mental constructs and processes. Although there is increasing progress toward the first two of these goals, models are only beginning to address the latter objective. By explicitly identifying gaps in knowledge, research programs can move deliberately and programmatically toward the goal of identifying brain representations underlying mental states and processes.
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Affiliation(s)
- Philip A Kragel
- Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of Colorado, Boulder, CO, USA; Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Leonie Koban
- Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of Colorado, Boulder, CO, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Tor D Wager
- Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of Colorado, Boulder, CO, USA.
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518
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Greene AS, Gao S, Scheinost D, Constable RT. Task-induced brain state manipulation improves prediction of individual traits. Nat Commun 2018; 9:2807. [PMID: 30022026 PMCID: PMC6052101 DOI: 10.1038/s41467-018-04920-3] [Citation(s) in RCA: 270] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 06/01/2018] [Indexed: 11/09/2022] Open
Abstract
Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.
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Affiliation(s)
- Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, 06520, CT, USA.
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, 06520, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, 06520, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, 06520, CT, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, 06520, CT, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, 06520, CT, USA
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519
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Rothlein D, DeGutis J, Esterman M. Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations. J Cogn Neurosci 2018; 30:1209-1228. [PMID: 30004852 DOI: 10.1162/jocn_a_01306] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not "all or none," but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.
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Affiliation(s)
| | | | - Michael Esterman
- VA Boston Healthcare System.,Boston University School of Medicine
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520
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Sayalı C, Badre D. Neural systems of cognitive demand avoidance. Neuropsychologia 2018; 123:41-54. [PMID: 29944865 DOI: 10.1016/j.neuropsychologia.2018.06.016] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 06/18/2018] [Accepted: 06/20/2018] [Indexed: 01/08/2023]
Abstract
Cognitive effort is typically aversive, evident in people's tendency to avoid cognitively demanding tasks. The 'cost of control' hypothesis suggests that engagement of cognitive control systems of the brain makes a task costly and the currency of that cost is a reduction in anticipated rewards. However, prior studies have relied on binary hard versus easy task subtractions to manipulate cognitive effort and so have not tested this hypothesis in "dose-response" fashion. In a sample of 50 participants, we parametrically manipulated the level of effort during fMRI scanning by systematically increasing cognitive control demands during a demand-selection paradigm over six levels. As expected, frontoparietal control network (FPN) activity increased, and reward network activity decreased, as control demands increased across tasks. However, avoidance behavior was not attributable to the change in FPN activity, lending only partial support to the cost of control hypothesis. By contrast, we unexpectedly observed that the de-activation of a task-negative brain network corresponding to the Default Mode Network (DMN) across levels of the cognitive control manipulation predicted the change in avoidance. In summary, we find partial support for the cost of control hypothesis, while highlighting the role of task-negative brain networks in modulating effort avoidance behavior.
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Affiliation(s)
- Ceyda Sayalı
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, United States.
| | - David Badre
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, United States; Carney Institute for Brain Sciences, Brown University, United States
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521
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Noble S, Spann MN, Tokoglu F, Shen X, Constable RT, Scheinost D. Influences on the Test-Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility. Cereb Cortex 2018; 27:5415-5429. [PMID: 28968754 PMCID: PMC6248395 DOI: 10.1093/cercor/bhx230] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 08/23/2017] [Indexed: 12/15/2022] Open
Abstract
Best practices are currently being developed for the acquisition and processing of
resting-state magnetic resonance imaging data used to estimate brain functional
organization—or “functional connectivity.” Standards have been proposed based on
test–retest reliability, but open questions remain. These include how amount of data per
subject influences whole-brain reliability, the influence of increasing runs versus
sessions, the spatial distribution of reliability, the reliability of multivariate
methods, and, crucially, how reliability maps onto prediction of behavior. We collected a
dataset of 12 extensively sampled individuals (144 min data each across 2 identically
configured scanners) to assess test–retest reliability of whole-brain connectivity within
the generalizability theory framework. We used Human Connectome Project data to replicate
these analyses and relate reliability to behavioral prediction. Overall, the historical
5-min scan produced poor reliability averaged across connections. Increasing the number of
sessions was more beneficial than increasing runs. Reliability was lowest for subcortical
connections and highest for within-network cortical connections. Multivariate reliability
was greater than univariate. Finally, reliability could not be used to improve prediction;
these findings are among the first to underscore this distinction for functional
connectivity. A comprehensive understanding of test–retest reliability, including its
limitations, supports the development of best practices in the field.
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Affiliation(s)
- Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
| | - Marisa N Spann
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Fuyuze Tokoglu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.,Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
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522
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Rohr CS, Vinette SA, Parsons KAL, Cho IYK, Dimond D, Benischek A, Lebel C, Dewey D, Bray S. Functional Connectivity of the Dorsal Attention Network Predicts Selective Attention in 4-7 year-old Girls. Cereb Cortex 2018; 27:4350-4360. [PMID: 27522072 DOI: 10.1093/cercor/bhw236] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 07/12/2016] [Indexed: 12/19/2022] Open
Abstract
Early childhood is a period of profound neural development and remodeling during which attention skills undergo rapid maturation. Attention networks have been extensively studied in the adult brain, yet relatively little is known about changes in early childhood, and their relation to cognitive development. We investigated the association between age and functional connectivity (FC) within the dorsal attention network (DAN) and the association between FC and attention skills in early childhood. Functional magnetic resonance imaging data was collected during passive viewing in 44 typically developing female children between 4 and 7 years whose sustained, selective, and executive attention skills were assessed. FC of the intraparietal sulcus (IPS) and the frontal eye fields (FEF) was computed across the entire brain and regressed against age. Age was positively associated with FC between core nodes of the DAN, the IPS and the FEF, and negatively associated with FC between the DAN and regions of the default-mode network. Further, controlling for age, FC between the IPS and FEF was significantly associated with selective attention. These findings add to our understanding of early childhood development of attention networks and suggest that greater FC within the DAN is associated with better selective attention skills.
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Affiliation(s)
- Christiane S Rohr
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4.,Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8
| | - Sarah A Vinette
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4.,Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8.,Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Kari A L Parsons
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8.,Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Ivy Y K Cho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4.,Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8.,Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Dennis Dimond
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8.,Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Alina Benischek
- Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8
| | - Catherine Lebel
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4.,Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8
| | - Deborah Dewey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8.,Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4.,Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada T2N 4Z6
| | - Signe Bray
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4.,Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada T3B 6A8.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada, T3B 6A8.,Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada T2N 1N4
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523
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Keerativittayayut R, Aoki R, Sarabi MT, Jimura K, Nakahara K. Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance. eLife 2018; 7:32696. [PMID: 29911970 PMCID: PMC6039182 DOI: 10.7554/elife.32696] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 06/16/2018] [Indexed: 12/19/2022] Open
Abstract
Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.
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Affiliation(s)
| | - Ryuta Aoki
- Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan
| | | | - Koji Jimura
- Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan.,Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Kiyoshi Nakahara
- School of Information, Kochi University of Technology, Kochi, Japan.,Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan
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524
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Cui Z, Gong G. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 2018; 178:622-637. [PMID: 29870817 DOI: 10.1016/j.neuroimage.2018.06.001] [Citation(s) in RCA: 171] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 05/31/2018] [Accepted: 06/01/2018] [Indexed: 12/27/2022] Open
Abstract
Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations.
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Affiliation(s)
- Zaixu Cui
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
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525
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Mattar MG, Thompson-Schill SL, Bassett DS. The network architecture of value learning. Netw Neurosci 2018; 2:128-149. [PMID: 30215030 PMCID: PMC6130435 DOI: 10.1162/netn_a_00021] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 07/03/2017] [Indexed: 01/11/2023] Open
Abstract
Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of four consecutive days. We show that connections between regions of the visual, frontal, and cingulate cortices become stronger as learning progresses, with some of these changes being specific to the type of feedback received during learning. These results demonstrate that functional networks dynamically track behavioral improvement in value judgments, and that interactions between network communities form predictive biomarkers of learning. Rational human behavior is the pursuit of actions that maximize expected reward. These rewards can be understood as stimulus-value contingencies, learned by experience throughout our lives. Various structures have been recognized to participate in these learning processes. Yet, an understanding of how these structures interact with one another and with other brain regions remains vastly unexplored. Here, we propose a novel analytical framework utilizing and extending techniques from the dynamic network neuroscience to ask “How do our brains change when we learn values?” We find that interactions between sensory and fronto-cingulate structures grow stronger as learning progresses, bringing together several isolated findings in the cognitive neuroscience of value-based behavior and extending our understanding of human learning in general.
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Affiliation(s)
- Marcelo G Mattar
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
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526
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Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative. Nat Commun 2018; 9:2043. [PMID: 29795116 PMCID: PMC5966466 DOI: 10.1038/s41467-018-04387-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 04/26/2018] [Indexed: 01/21/2023] Open
Abstract
Individuals often interpret the same event in different ways. How do personality traits modulate brain activity evoked by a complex stimulus? Here we report results from a naturalistic paradigm designed to draw out both neural and behavioral variation along a specific dimension of interest, namely paranoia. Participants listen to a narrative during functional MRI describing an ambiguous social scenario, written such that some individuals would find it highly suspicious, while others less so. Using inter-subject correlation analysis, we identify several brain areas that are differentially synchronized during listening between participants with high and low trait-level paranoia, including theory-of-mind regions. Follow-up analyses indicate that these regions are more active to mentalizing events in high-paranoia individuals. Analyzing participants’ speech as they freely recall the narrative reveals semantic and syntactic features that also scale with paranoia. Results indicate that a personality trait can act as an intrinsic “prime,” yielding different neural and behavioral responses to the same stimulus across individuals. Reactions to the same event can vary vastly based on multiple factors. Here the authors show that people with high trait-level paranoia process ambiguous information in a narrative differently and this can be attributed to greater activity in mentalizing brain regions during the moments of ambiguity.
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527
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Wang J, Hao Z, Wang H. Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation. Front Hum Neurosci 2018; 12:166. [PMID: 29780309 PMCID: PMC5945868 DOI: 10.3389/fnhum.2018.00166] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 04/10/2018] [Indexed: 11/13/2022] Open
Abstract
The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.
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Affiliation(s)
- J Wang
- School of Mathematics and Big Data, Foshan University, Foshan, China.,Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
| | - Z Hao
- School of Mathematics and Big Data, Foshan University, Foshan, China
| | - H Wang
- School of Mathematics and Big Data, Foshan University, Foshan, China
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528
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Mattar MG, Wymbs NF, Bock AS, Aguirre GK, Grafton ST, Bassett DS. Predicting future learning from baseline network architecture. Neuroimage 2018; 172:107-117. [PMID: 29366697 PMCID: PMC5910215 DOI: 10.1016/j.neuroimage.2018.01.037] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 01/09/2018] [Accepted: 01/15/2018] [Indexed: 12/24/2022] Open
Abstract
Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.
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Affiliation(s)
- Marcelo G Mattar
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Nicholas F Wymbs
- Human Brain Physiology and Stimulation Laboratory, Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, MD, USA
| | - Andrew S Bock
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Geoffrey K Aguirre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences and UCSB Brain Imaging Center, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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529
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Feng C, Yuan J, Geng H, Gu R, Zhou H, Wu X, Luo Y. Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity. Hum Brain Mapp 2018; 39:3701-3712. [PMID: 29749072 DOI: 10.1002/hbm.24205] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 04/05/2018] [Accepted: 04/23/2018] [Indexed: 01/16/2023] Open
Abstract
Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole-brain resting-state functional connectivity (RSFC). Resting-state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole-brain RSFC and trait narcissism. We demonstrated that the machine-learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level.
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Affiliation(s)
- Chunliang Feng
- College of Information Science and Technology, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China
| | - Jie Yuan
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Haiyang Geng
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China
| | - Ruolei Gu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Hui Zhou
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Yuejia Luo
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China
- Center for Emotion and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
- Depatment of Psychology, Southern Medical University, Guangzhou, China
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530
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Colclough GL, Woolrich MW, Harrison SJ, Rojas López PA, Valdes-Sosa PA, Smith SM. Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks. Neuroimage 2018; 178:370-384. [PMID: 29746906 PMCID: PMC6565932 DOI: 10.1016/j.neuroimage.2018.04.077] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 03/28/2018] [Accepted: 04/30/2018] [Indexed: 01/21/2023] Open
Abstract
A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.
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Affiliation(s)
- Giles L Colclough
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Centre for Doctoral Training in Healthcare Innovation, Institute of Biomedical Engineering Science, Department of Engineering, University of Oxford, Oxford, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Samuel J Harrison
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Pedro A Rojas López
- Neuroinformatics Department, El Centro de Neurociencias de Cuba (CNEURO), La Habana, Cuba
| | - Pedro A Valdes-Sosa
- Neuroinformatics Department, El Centro de Neurociencias de Cuba (CNEURO), La Habana, Cuba; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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531
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Wen T, Liu DC, Hsieh S. Connectivity patterns in cognitive control networks predict naturalistic multitasking ability. Neuropsychologia 2018; 114:195-202. [PMID: 29729277 DOI: 10.1016/j.neuropsychologia.2018.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 04/24/2018] [Accepted: 05/01/2018] [Indexed: 10/17/2022]
Abstract
Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance.
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Affiliation(s)
- Tanya Wen
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, CB2 7EF Cambridge, United Kingdom.
| | - De-Cyuan Liu
- Department of Psychology, Asia University, Taichung, Taiwan.
| | - Shulan Hsieh
- Department of Psychology, National Cheng Kung University, 1 University Road, 70101, Tainan, Taiwan; Institute of Allied Health Sciences, National Cheng Kung University, Tainan, Taiwan; Department of Public Health, National Cheng Kung University, Tainan, Taiwan.
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532
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Cui Z, Su M, Li L, Shu H, Gong G. Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume. Cereb Cortex 2018; 28:1656-1672. [PMID: 28334252 PMCID: PMC6669415 DOI: 10.1093/cercor/bhx061] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 02/19/2017] [Accepted: 02/23/2017] [Indexed: 12/23/2022] Open
Abstract
Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.
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Affiliation(s)
- Zaixu Cui
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mengmeng Su
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Liangjie Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hua Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
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533
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Esterman M, Poole V, Liu G, DeGutis J. Modulating Reward Induces Differential Neurocognitive Approaches to Sustained Attention. Cereb Cortex 2018; 27:4022-4032. [PMID: 27473320 DOI: 10.1093/cercor/bhw214] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 06/15/2016] [Indexed: 11/14/2022] Open
Abstract
Reward and motivation have powerful effects on cognition and brain activity, yet it remains unclear how they affect sustained cognitive performance. We have recently shown that a variety of motivators improve accuracy and reduce variability during sustained attention. In the current study, we investigate how neural activity in task-positive networks supports these sustained attention improvements. Participants performed the gradual-onset continuous performance task with alternating motivated (rewarded) and unmotivated (unrewarded) blocks. During motivated blocks, we observed increased sustained neural recruitment of task-positive regions, which interacted with fluctuations in task performance. Specifically, during motivated blocks, participants recruited these regions in preparation for upcoming targets, and this activation predicted accuracy. In contrast, during unmotivated blocks, no such advanced preparation was observed. Furthermore, during motivated blocks, participants had similar activation levels during both optimal (in-the-zone) and suboptimal (out-of-the-zone) epochs of performance. In contrast, during unmotivated blocks, task-positive regions were only engaged to a similar degree as motivated blocks during suboptimal (out-of-the-zone) periods. These data support a framework in which motivated individuals act as "cognitive investors," engaging task-positive resources proactively and consistently during sustaining attention. When unmotivated, however, the same individuals act as "cognitive misers," engaging maximal task-positive resources only during periods of struggle.
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Affiliation(s)
- Michael Esterman
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA 02130, USA.,Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, MA 02130, USA.,Geriatric Research Education and Clinical Center (GRECC), Boston Division VA Healthcare System, Boston, MA 02130, USA.,Department of Psychiatry, Boston University School of Medicine, Boston, MA 02118, USA
| | - Victoria Poole
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA 02130, USA.,Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, MA 02130, USA.,Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Guanyu Liu
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Joseph DeGutis
- Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, MA 02130, USA.,Geriatric Research Education and Clinical Center (GRECC), Boston Division VA Healthcare System, Boston, MA 02130, USA.,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
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534
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A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity. Neuroimage 2018; 172:506-516. [DOI: 10.1016/j.neuroimage.2018.01.080] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 01/27/2018] [Accepted: 01/30/2018] [Indexed: 01/18/2023] Open
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535
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Dynamic functional connectivity and its behavioral correlates beyond vigilance. Neuroimage 2018; 177:1-10. [PMID: 29704612 DOI: 10.1016/j.neuroimage.2018.04.049] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Accepted: 04/21/2018] [Indexed: 11/22/2022] Open
Abstract
Fluctuations in resting-state functional connectivity and global signal have been found to correspond with vigilance fluctuations, but their associations with other behavioral measures are unclear. We evaluated 52 healthy adolescents after a week of adequate sleep followed by five nights of sleep restriction to unmask inter-individual differences in cognition and mood. Resting state scans obtained at baseline only, analyzed using sliding window analysis, consistently yielded two polar dynamic functional connectivity states (DCSs) corresponding to previously reported 'low arousal' and 'high arousal' states. We found that the relative temporal preponderance of two dynamic connectivity states (DCS) in well-rested participants, indexed by a median split of participants, based on the relative time spent in these DCS, revealed highly significant group differences in vigilance at baseline and its decline following multiple nights of sleep restriction. Group differences in processing speed and working memory following manipulation but not at baseline suggest utility of DCS in predicting cognitive vulnerabilities unmasked by a stressor like sleep restriction. DCS temporal predominance was uninformative about mood and sleepiness speaking to specificity in its behavioral predictions. Global signal fluctuation provided information confined to vigilance. This appears to be related to head motion, which increases during periods of low arousal.
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536
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Liu Z, Zhang J, Zhang K, Zhang J, Li X, Cheng W, Li M, Zhao L, Deng W, Guo W, Ma X, Wang Q, Matthews PM, Feng J, Li T. Distinguishable brain networks relate disease susceptibility to symptom expression in schizophrenia. Hum Brain Mapp 2018; 39:3503-3515. [PMID: 29691943 DOI: 10.1002/hbm.24190] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 03/18/2018] [Accepted: 04/06/2018] [Indexed: 02/05/2023] Open
Abstract
Disease association studies have characterized altered resting-state functional connectivities describing schizophrenia, but failed to model symptom expression well. We developed a model that could account for symptom severity and meanwhile relate this to disease-related functional pathology. We correlated BOLD signal across brain regions and tested separately for associations with disease (disease edges) and with symptom severity (symptom edges) in a prediction-based scheme. We then integrated them in an "edge bi-color" graph, and adopted mediation analysis to test for causality between the disease and symptom networks and symptom scores. For first-episode schizophrenics (FES, 161 drug-naïve patients and 150 controls), the disease network (with inferior frontal gyrus being the hub) and the symptom-network (posterior occipital-parietal cortex being the hub) were found to overlap in the temporal lobe. For chronic schizophrenis (CS, 69 medicated patients and 62 controls), disease network was dominated by thalamocortical connectivities, and overlapped with symptom network in the middle frontal gyrus. We found that symptom network mediates the relationship between disease network and symptom scores in FEP, but was unable to define a relationship between them for the smaller CS population. Our results suggest that the disease network distinguishing core functional pathology in resting-state brain may be responsible for symptom expression in FES through a wider brain network associated with core symptoms. We hypothesize that top-down control from heteromodal prefrontal cortex to posterior transmodal cortex contributes to positive symptoms of schizophrenia. Our work also suggests differences in mechanisms of symptom expression between FES and CS, highlighting a need to distinguish between these groups.
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Affiliation(s)
- Zhaowen Liu
- School of Computer Science and Technology, Xidian University, Xi'an, Shannxi, 710071, People's Republic of China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China.,Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, People's Republic of China
| | - Kai Zhang
- Department of Computer and Information Sciences, Temple University, 1801 North Broad Street, Philadelphia, Pennsylvania, 1912
| | - Junying Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, Shannxi, 710071, People's Republic of China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China
| | - Mingli Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Wanjun Guo
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
| | - Paul M Matthews
- Division of Brain Sciences, Department of Medicine and Centre for Neurotechnology, Imperial College, London, W12 0NN, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China.,Shanghai Center for Mathematical Sciences, Shanghai, 200433, People's Republic of China.,Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.,Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, People's Republic of China.,Zhongshan Hosipital, Fudan University, Shanghai, 200433, People's Republic of China
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China
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537
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Salehi M, Karbasi A, Shen X, Scheinost D, Constable RT. An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks. Neuroimage 2018; 170:54-67. [PMID: 28882628 PMCID: PMC5905726 DOI: 10.1016/j.neuroimage.2017.08.068] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 06/09/2017] [Accepted: 08/24/2017] [Indexed: 01/09/2023] Open
Abstract
Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.
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Affiliation(s)
- Mehraveh Salehi
- Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA.
| | - Amin Karbasi
- Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
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538
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Lin Q, Rosenberg MD, Yoo K, Hsu TW, O'Connell TP, Chun MM. Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease. Front Aging Neurosci 2018; 10:94. [PMID: 29706883 PMCID: PMC5908906 DOI: 10.3389/fnagi.2018.00094] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 03/19/2018] [Indexed: 01/11/2023] Open
Abstract
Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.
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Affiliation(s)
- Qi Lin
- Department of Psychology, Yale University, New Haven, CT, United States
| | | | - Kwangsun Yoo
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Tiffany W Hsu
- Department of Psychology, Yale University, New Haven, CT, United States
| | | | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT, United States.,Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States.,Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
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539
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Anderson KM, Krienen FM, Choi EY, Reinen JM, Yeo BTT, Holmes AJ. Gene expression links functional networks across cortex and striatum. Nat Commun 2018; 9:1428. [PMID: 29651138 PMCID: PMC5897339 DOI: 10.1038/s41467-018-03811-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 03/14/2018] [Indexed: 12/12/2022] Open
Abstract
The human brain is comprised of a complex web of functional networks that link anatomically distinct regions. However, the biological mechanisms supporting network organization remain elusive, particularly across cortical and subcortical territories with vastly divergent cellular and molecular properties. Here, using human and primate brain transcriptional atlases, we demonstrate that spatial patterns of gene expression show strong correspondence with limbic and somato/motor cortico-striatal functional networks. Network-associated expression is consistent across independent human datasets and evolutionarily conserved in non-human primates. Genes preferentially expressed within the limbic network (encompassing nucleus accumbens, orbital/ventromedial prefrontal cortex, and temporal pole) relate to risk for psychiatric illness, chloride channel complexes, and markers of somatostatin neurons. Somato/motor associated genes are enriched for oligodendrocytes and markers of parvalbumin neurons. These analyses indicate that parallel cortico-striatal processing channels possess dissociable genetic signatures that recapitulate distributed functional networks, and nominate molecular mechanisms supporting cortico-striatal circuitry in health and disease. The functional connectivity of brain regions can be reflected in a shared molecular architecture. This cross-modal study demonstrates correspondence of spatial patterns of gene expression to limbic and somato/motor cortico-striatal networks in human and non-human primates.
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Affiliation(s)
- Kevin M Anderson
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Fenna M Krienen
- Department of Genetics, Harvard Medical School, Boston, MA, 02114, USA
| | - Eun Young Choi
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
| | - Jenna M Reinen
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Network Programme, National University of Singapore, Singapore, 117456, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Charlestown, MA, 02129, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Charlestown, MA, 02129, USA. .,Department of Psychiatry, Yale University, New Haven, CT, 06520, USA. .,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA.
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540
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Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:4835676. [PMID: 29849548 PMCID: PMC5914150 DOI: 10.1155/2018/4835676] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/23/2018] [Accepted: 03/01/2018] [Indexed: 01/21/2023]
Abstract
Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.
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541
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Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology. Neuroimage 2018; 169:395-406. [DOI: 10.1016/j.neuroimage.2017.12.030] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 11/22/2017] [Accepted: 12/11/2017] [Indexed: 12/18/2022] Open
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542
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Rohr CS, Arora A, Cho IYK, Katlariwala P, Dimond D, Dewey D, Bray S. Functional network integration and attention skills in young children. Dev Cogn Neurosci 2018; 30:200-211. [PMID: 29587178 PMCID: PMC6969078 DOI: 10.1016/j.dcn.2018.03.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/12/2018] [Accepted: 03/15/2018] [Indexed: 12/17/2022] Open
Abstract
Children acquire attention skills rapidly during early childhood as their brains undergo vast neural development. Attention is well studied in the adult brain, yet due to the challenges associated with scanning young children, investigations in early childhood are sparse. Here, we examined the relationship between age, attention and functional connectivity (FC) during passive viewing in multiple intrinsic connectivity networks (ICNs) in 60 typically developing girls between 4 and 7 years whose sustained, selective and executive attention skills were assessed. Visual, auditory, sensorimotor, default mode (DMN), dorsal attention (DAN), ventral attention (VAN), salience, and frontoparietal ICNs were identified via Independent Component Analysis and subjected to a dual regression. Individual spatial maps were regressed against age and attention skills, controlling for age. All ICNs except the VAN showed regions of increasing FC with age. Attention skills were associated with FC in distinct networks after controlling for age: selective attention positively related to FC in the DAN; sustained attention positively related to FC in visual and auditory ICNs; and executive attention positively related to FC in the DMN and visual ICN. These findings suggest distributed network integration across this age range and highlight how multiple ICNs contribute to attention skills in early childhood.
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Affiliation(s)
- Christiane S Rohr
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
| | - Anish Arora
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Ivy Y K Cho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Prayash Katlariwala
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Dennis Dimond
- Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Deborah Dewey
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Signe Bray
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; Child and Adolescent Imaging Research Program, University of Calgary, Calgary, Alberta, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
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543
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544
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Sinitsyn DO, Legostaeva LA, Kremneva EI, Morozova SN, Poydasheva AG, Mochalova EG, Chervyakova OG, Ryabinkina JV, Suponeva NA, Piradov MA. Degrees of functional connectome abnormality in disorders of consciousness. Hum Brain Mapp 2018; 39:2929-2940. [PMID: 29575425 DOI: 10.1002/hbm.24050] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 03/06/2018] [Accepted: 03/08/2018] [Indexed: 12/26/2022] Open
Abstract
Understanding the neuronal basis of disorders of consciousness can help improve the accuracy of their diagnosis, indicate potential targets for therapeutic interventions, and provide insights into the organization of normal conscious information processing. Measurements of brain activity have been used to find associations of the levels of consciousness with brain complexity, topological features of functional connectomes, and disruption of resting-state networks. However, obtainment of a detailed picture of activity patterns underlying the vegetative state/unresponsive wakefulness syndrome and the minimally conscious state remains a work in progress. We here aimed at finding the aspects of fMRI-based functional connectivity that differentiate these states from each other and from the normal condition. A group of 22 patients was studied (9 minimally conscious state and 13 vegetative state/unresponsive wakefulness syndrome). Patients were shown to have reduced connectivity in most resting-state networks and disrupted patterns of relative connection strengths as compared to healthy subjects. Differences between the unresponsive wakefulness syndrome and the minimally conscious state were found in the patterns formed by a relatively small number of strongest positive correlations selected by thresholding. These differences were captured by measures of functional connectivity disruption that integrate area-specific abnormalities over the whole brain. The results suggest that the strong positive correlations between the functional activities of specific brain areas observed in healthy individuals may be critical for consciousness and be an important target of disruption in disorders of consciousness.
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Affiliation(s)
- Dmitry O Sinitsyn
- Research Center of Neurology, 80 Volokolamskoe shosse, Moscow, 125367, Russia
| | | | - Elena I Kremneva
- Research Center of Neurology, 80 Volokolamskoe shosse, Moscow, 125367, Russia
| | - Sofya N Morozova
- Research Center of Neurology, 80 Volokolamskoe shosse, Moscow, 125367, Russia
| | | | | | | | - Julia V Ryabinkina
- Research Center of Neurology, 80 Volokolamskoe shosse, Moscow, 125367, Russia
| | - Natalia A Suponeva
- Research Center of Neurology, 80 Volokolamskoe shosse, Moscow, 125367, Russia
| | - Michael A Piradov
- Research Center of Neurology, 80 Volokolamskoe shosse, Moscow, 125367, Russia
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545
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Nostro AD, Müller VI, Varikuti DP, Pläschke RN, Hoffstaedter F, Langner R, Patil KR, Eickhoff SB. Predicting personality from network-based resting-state functional connectivity. Brain Struct Funct 2018; 223:2699-2719. [PMID: 29572625 DOI: 10.1007/s00429-018-1651-z] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 03/12/2018] [Indexed: 12/20/2022]
Abstract
Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender.
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Affiliation(s)
- Alessandra D Nostro
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany. .,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany. .,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany.
| | - Veronika I Müller
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Deepthi P Varikuti
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Rachel N Pläschke
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Robert Langner
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1,7), Research Centre Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
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546
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Reinen JM, Chén OY, Hutchison RM, Yeo BTT, Anderson KM, Sabuncu MR, Öngür D, Roffman JL, Smoller JW, Baker JT, Holmes AJ. The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nat Commun 2018; 9:1157. [PMID: 29559638 PMCID: PMC5861099 DOI: 10.1038/s41467-018-03462-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 02/15/2018] [Indexed: 02/07/2023] Open
Abstract
Higher-order cognition emerges through the flexible interactions of large-scale brain networks, an aspect of temporal coordination that may be impaired in psychosis. Here, we map the dynamic functional architecture of the cerebral cortex in healthy young adults, leveraging this atlas of transient network configurations (states), to identify state- and network-specific disruptions in patients with schizophrenia and psychotic bipolar disorder. We demonstrate that dynamic connectivity profiles are reliable within participants, and can act as a fingerprint, identifying specific individuals within a larger group. Patients with psychotic illness exhibit intermittent disruptions within cortical networks previously associated with the disease, and the individual connectivity profiles within specific brain states predict the presence of active psychotic symptoms. Taken together, these results provide evidence for a reconfigurable dynamic architecture in the general population and suggest that prior reports of network disruptions in psychosis may reflect symptom-relevant transient abnormalities, rather than a time-invariant global deficit. Temporal changes in brain dynamics are linked with cognitive abilities, but neither their stability nor relationship to psychosis is clear. Here, authors describe the dynamic neural architecture in healthy controls and patients with psychosis and find that they are stable over time and can predict psychotic symptoms.
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Affiliation(s)
- Jenna M Reinen
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Oliver Y Chén
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | | | - B T Thomas Yeo
- Department of Electrical & Computer Engineering, Clinical Imaging Research Centre, Singapore Institute for Neurotechnology & Memory Network Programme, National University of Singapore, Singapore, 117583, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, CT, 06520, USA
| | - Mert R Sabuncu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.,School of Electrical and Computer Engineering and Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, 14853, USA
| | - Dost Öngür
- Department of Psychiatry, Psychotic Disorders Division, McLean Hospital, Belmont, MA, 02478, USA
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.,Psychiatric Neuroimaging Research Division, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Justin T Baker
- Department of Psychiatry, Psychotic Disorders Division, McLean Hospital, Belmont, MA, 02478, USA
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA. .,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. .,Department of Psychiatry, Yale University, New Haven, CT, 06511, USA.
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547
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Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT, Kandala S, Estes AM, Botteron KN, Collins L, Dager SR, Evans AC, Gerig G, Gu H, McKinstry RC, Paterson S, Schultz RT, Styner M, Schlaggar BL, Pruett JR, Piven J. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med 2018; 9:9/393/eaag2882. [PMID: 28592562 DOI: 10.1126/scitranslmed.aag2882] [Citation(s) in RCA: 201] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 11/15/2016] [Accepted: 02/24/2017] [Indexed: 12/30/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.
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Affiliation(s)
- Robert W Emerson
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA.
| | - Chloe Adams
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tomoyuki Nishino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Heather Cody Hazlett
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jason J Wolff
- Department of Educational Psychology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lonnie Zwaigenbaum
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - John N Constantino
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Mark D Shen
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
| | - Meghan R Swanson
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Annette M Estes
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA 98105, USA
| | - Kelly N Botteron
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.,Mallinckrodt Institute of Radiology, Washington University, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Louis Collins
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Stephen R Dager
- Center on Human Development and Disability, University of Washington, Seattle, WA 98105, USA.,Department of Radiology, University of Washington, Seattle, WA 98105, USA
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Guido Gerig
- Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
| | - Hongbin Gu
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
| | - Robert C McKinstry
- Mallinckrodt Institute of Radiology, Washington University, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Sarah Paterson
- Center for Autism Research, Department of Pediatrics, Children's Hospital of Philadelphia, and University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert T Schultz
- Center for Autism Research, Department of Pediatrics, Children's Hospital of Philadelphia, and University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martin Styner
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
| | | | - Bradley L Schlaggar
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.,Mallinckrodt Institute of Radiology, Washington University, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John R Pruett
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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548
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Ruiz-Rizzo AL, Neitzel J, Müller HJ, Sorg C, Finke K. Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks. Front Hum Neurosci 2018; 12:89. [PMID: 29662444 PMCID: PMC5890144 DOI: 10.3389/fnhum.2018.00089] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 02/23/2018] [Indexed: 02/04/2023] Open
Abstract
Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention parameters that are assumed to constitute relatively stable traits correspond distinctly to the functional connectivity both within and between particular functional networks. This implies that individual differences in basic attention functions are represented by differences in the coherence of slowly fluctuating brain activity.
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Affiliation(s)
- Adriana L Ruiz-Rizzo
- Department of General and Experimental Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.,Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Julia Neitzel
- Department of General and Experimental Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.,Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Hermann J Müller
- Department of General and Experimental Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.,School of Psychological Science, Birkbeck College, University of London, London, United Kingdom
| | - Christian Sorg
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany.,Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Kathrin Finke
- Department of General and Experimental Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.,Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany.,Hans-Berger Department of Neurology, Friedrich Schiller University Jena, Jena, Germany
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549
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Liu Z, Zhang J, Xie X, Rolls ET, Sun J, Zhang K, Jiao Z, Chen Q, Zhang J, Qiu J, Feng J. Neural and genetic determinants of creativity. Neuroimage 2018. [PMID: 29518564 DOI: 10.1016/j.neuroimage.2018.02.067] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity. Using the Torrance Tests of Creative Thinking score, we found that high figural creativity is mainly related to high functional connectivity between the executive control, attention, and memory retrieval networks (strong top-down effects); and to low functional connectivity between the default mode network, the ventral attention network, and the subcortical and primary sensory networks (weak bottom-up processing) in the first dataset (consisting of 138 subjects). High creativity also correlates significantly with mutations of genes coding for both excitatory and inhibitory neurotransmitters. Combining the brain connectome and the genomic data we can predict individuals' creativity scores with an accuracy of 78.4%, which is significantly better than prediction using single modality data (gene or functional connectivity), indicating the importance of combining multi-modality data. Our neuroimaging prediction model built upon the first dataset was cross-validated by a completely new dataset of 98 subjects (r = 0.267, p = 0.0078) with an accuracy of 64.6%. In addition, the creativity-related functional connectivity network we identified in the first dataset was still significantly correlated with the creativity score in the new dataset (p<10-3). In summary, our research demonstrates that strong top-down control versus weak bottom-up processes underlie creativity, which is modulated by competition between the glutamate and GABA neurotransmitter systems. Our work provides the first insights into both the neural and the genetic bases of creativity.
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Affiliation(s)
- Zhaowen Liu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shannxi, PR China
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, PR China.
| | - Xiaohua Xie
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, PR China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Oxford Centre for Computational Neuroscience, Oxford UK
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, PR China; School of Psychology, Southwest University (SWU), Chongqing, PR China
| | - Kai Zhang
- Department of Computer and Information Sciences, Temple University, 1801 North Broad Street, Philadelphia, PA 19122, USA
| | - Zeyu Jiao
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, PR China; Shanghai Center for Mathematical Sciences, Shanghai, 200433, PR China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, PR China; School of Psychology, Southwest University (SWU), Chongqing, PR China
| | - Junying Zhang
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shannxi, PR China.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, PR China; School of Psychology, Southwest University (SWU), Chongqing, PR China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing 100875, PR China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, PR China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, PR China; Shanghai Center for Mathematical Sciences, Shanghai, 200433, PR China; Zhongshan Hospital, Fudan University, Shanghai, 200433, PR China.
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550
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Mason SL, Daws RE, Soreq E, Johnson EB, Scahill RI, Tabrizi SJ, Barker RA, Hampshire A. Predicting clinical diagnosis in Huntington's disease: An imaging polymarker. Ann Neurol 2018; 83:532-543. [PMID: 29405351 PMCID: PMC5900832 DOI: 10.1002/ana.25171] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 02/01/2018] [Accepted: 02/01/2018] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real-life clinical diagnosis in HD. METHOD A multivariate machine learning approach was applied to resting-state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross-group comparisons between preHD and controls, and within the preHD group in relation to "estimated" and "actual" proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. RESULTS Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. INTERPRETATION We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532-543.
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Affiliation(s)
- Sarah L. Mason
- John Van Geest Centre for Brain RepairUniversity of CambridgeUnited Kingdom
| | - Richard E. Daws
- The Computational, Cognitive & Clinical Neuroimaging Laboratory (CNL), Division of Brain SciencesImperial College LondonUnited Kingdom
| | - Eyal Soreq
- The Computational, Cognitive & Clinical Neuroimaging Laboratory (CNL), Division of Brain SciencesImperial College LondonUnited Kingdom
| | - Eileanoir B. Johnson
- Huntington's Disease Research CentreUCL Institute of Neurology, University College LondonUnited Kingdom
| | - Rachael I. Scahill
- Huntington's Disease Research CentreUCL Institute of Neurology, University College LondonUnited Kingdom
| | - Sarah J. Tabrizi
- Huntington's Disease Research CentreUCL Institute of Neurology, University College LondonUnited Kingdom
| | - Roger A. Barker
- John Van Geest Centre for Brain RepairUniversity of CambridgeUnited Kingdom
- Department of Clinical NeuroscienceUniversity of CambridgeUnited Kingdom
| | - Adam Hampshire
- The Computational, Cognitive & Clinical Neuroimaging Laboratory (CNL), Division of Brain SciencesImperial College LondonUnited Kingdom
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